http://dspace.bsu.edu.ru/handle/123456789/64204| Title: | Machine learning assisted design of new ductile high-entropy alloys: Application to Al-Cr-Nb-Ti-V-Zr system |
| Authors: | Klimenko, D. Stepanov, N. Ryltsev, R. Yurchenko, N. Zherebtsov, S. |
| Keywords: | technique metal science high-entropy alloys machine learning data plasticity phenomenological models strength |
| Issue Date: | 2024 |
| Citation: | Machine learning assisted design of new ductile high-entropy alloys: Application to Al-Cr-Nb-Ti-V-Zr system / D. Klimenko, N. Stepanov, R. Ryltsev [et al.] // Intermetallics. - 2024. - Vol.175.-Art. 108469. - URL: https://www.sciencedirect.com/science/article/pii/S0966979524002887. |
| Abstract: | The search for new high-entropy alloys (HEAs) with desired properties is an urgent problem that is hardly solvable experimentally due to the extremely large number of possible alloy compositions. Here we address developing data-driven machine learning models (DDML) to predict the ductility of HEAs |
| URI: | http://dspace.bsu.edu.ru/handle/123456789/64204 |
| Appears in Collections: | Статьи из периодических изданий и сборников (на иностранных языках) = Articles from periodicals and collections (in foreign languages) |
| File | Description | Size | Format | |
|---|---|---|---|---|
| Klimenko_Machine_24.pdf | 490.22 kB | Adobe PDF | View/Open |
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